Table REF  shows the performance of the proposed pre-training network Voxel-MAE on KITTI val set. Although the number of samples in KITTI is small, our Voxel-MAE can still improve SECOND [1]} and PV-RCNN [2]}, especially for the small objects (i.e., pedestrian and cyclist). We also find that our Voxel-MAE can improve the evaluation of the bounding box and orientation a lot, as shown in Table REF . This is because our voxel masking strategy can help to learn high-level semantic information as the Voxel-MAE needs to recover these totally missing voxels. The bounding box and orientation predicting will benefit from such a design.
